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首页> 外文会议>IEEE International Conference on Acoustics Speech and Signal;ICASSP 2010 >A comparison of supervised and unsupervised cross-lingual speaker adaptation approaches for HMM-based speech synthesis
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A comparison of supervised and unsupervised cross-lingual speaker adaptation approaches for HMM-based speech synthesis

机译：基于HMM的语音合成的有监督和无监督跨语言说话者自适应方法的比较

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摘要

The EMIME project aims to build a personalized speech-to-speech translator, such that spoken input of a user in one language is used to produce spoken output that still sounds like the user's voice however in another language. This distinctiveness makes unsupervised cross-lingual speaker adaptation one key to the project's success. So far, research has been conducted into unsupervised and cross-lingual cases separately by means of decision tree marginalization and HMM state mapping respectively. In this paper we combine the two techniques to perform unsupervised cross-lingual speaker adaptation. The performance of eight speaker adaptation systems (supervised vs. unsupervised, intra-lingual vs. cross-lingual) is compared using objective and subjective evaluations. Experimental results show the performance of unsupervised cross-lingual speaker adaptation is comparable to that of the supervised case in terms of spectrum adaptation in the EMIME scenario, even though automatically obtained transcriptions have a very high phoneme error rate.
机译：EMIME项目旨在构建个性化的语音到语音翻译器，以使用户使用一种语言的语音输入产生语音输出，但仍然听起来像用户的语音，但是使用另一种语言。这种独特性使无人监督的跨语言演讲者适应成为该项目成功的关键。到目前为止，已经分别通过决策树边缘化和HMM状态映射分别对无监督和跨语言的案例进行了研究。在本文中，我们结合了两种技术来执行无监督的跨语言说话者自适应。使用客观评估和主观评估来比较八个说话者适应系统（有监督与无监督，语言内与跨语言）的性能。实验结果表明，即使自动获得的转录具有很高的音素错误率，在EMIME场景中，无监督的跨语言说话者适应的性能在监督适应方面也可与有监督的情况相媲美。

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